Resilience¶
$$ Resilience = \frac{Readiness*(1-Vulnerability)}{Readiness + (1- Vulnerability)}$$
Correlation between our two KPI¶
In [49]:
Ready_Vulnerability
No clear linear relation between the Readiness and the Vulnerability of a city.
Map of the cities split into three categories according to their Readiness¶
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m
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Make this Notebook Trusted to load map: File -> Trust Notebook
Red : Poorly Prepared to withstand a shock Orange : Midly Prepared to withstand a shock Green : Fully Prepared to withstand a shock
Too many questions and features in our initial survey that lead to too many missing answers¶
A need to decrease the number of questions for our survey to be more user-friendly and quicker to fill¶
We therefore implemented a feature selection¶
In [62]:
Targets.show()
In [68]:
plt.barh([x for x in range(len(importance))], importance, tick_label = X.columns)
plt.title('Features importance for Vulnerability');
In [71]:
plt.barh([x for x in range(len(importance))], importance, tick_label = X.columns)
plt.title('Features importance for Readiness');
SVM model for features selection according to our Targets¶
SVM model for Readiness¶
In [144]:
plt.figure()
plt.xlabel("Number of features selected")
plt.ylabel("Cross validation score (nb of correct classifications)")
plt.plot(range(min_features_to_select, len(rfecv1.grid_scores_) + min_features_to_select),rfecv1.grid_scores_)
plt.show()
SVM model for Vulnerability¶
In [149]:
plt.figure()
plt.xlabel("Number of features selected")
plt.ylabel("Cross validation score (nb of correct classifications)")
plt.plot(range(min_features_to_select,len(rfecv.grid_scores_) + min_features_to_select),rfecv.grid_scores_)
plt.show()
Pipeline and ML models¶
We selected 10 features from our analysis to train our Machine Learning model on¶
